ACCS-UAA Covariate qc and data exploration
Document last updated 2021-12-03 12:47:57 by Dustin Merrigan (dwmerrigan@alaska.edu)
Replace all nan with 0
# clear environment
rm(list=ls())
library(pacman)
p_load(DataExplorer,tidyverse,lubridate,readr,readxl,hms,plotly,DT,stringr,
janitor,ggplot2)
# Read In Datasets Examine datasets AKSSF_Covariates.csv and AKSSF_sites_sj_maxfac.csv using descriptive statistics, plots, and in maps where appropriate.
getwd()## [1] "C:/Users/dwmerrigan/Documents/GitHub/AKSSF/data_preparation/sensitivity_drivers"#Data from covariates script
cov.df.raw <- read.csv(file = 'AKSSF_Covariates.csv', header = TRUE)
#Data from spatial join script
sites.df <- read.csv(file = 'AKSSF_sites_sj_maxfac.csv', header = TRUE)
#Data from Modis Script
lcld.df <- read.csv(file = 'AKSSF_wtd_lcld_mn.csv', header = TRUE)
lcld_cols <- c("wtd_lcld_mn_2001","wtd_lcld_mn_2002", "wtd_lcld_mn_2003",
"wtd_lcld_mn_2004","wtd_lcld_mn_2005", "wtd_lcld_mn_2006",
"wtd_lcld_mn_2007", "wtd_lcld_mn_2008", "wtd_lcld_mn_2009",
"wtd_lcld_mn_2010", "wtd_lcld_mn_2011", "wtd_lcld_mn_2012",
"wtd_lcld_mn_2013","wtd_lcld_mn_2014", "wtd_lcld_mn_2015",
"wtd_lcld_mn_2016", "wtd_lcld_mn_2017", "wtd_lcld_mn_2018",
"wtd_lcld_mn_2019")
dput(colnames(sites.df))## c("ï..OBJECTID", "SiteID", "cat_ID_con", "area_diff", "lat",
## "lon", "site_max_acc", "site_acc_sqKm", "Area_km2", "str_slope",
## "str_ord", "dist_coast_km", "ds_dist_outlet")site_keep_cols <- c("SiteID","region", "cat_ID_con", "site_max_acc", "site_acc_sqKm",
"Area_km2","area_diff","size_diff", "lat", "lon",
'str_ord','str_slope',"dist_coast_km","ds_dist_outlet")
sites.df <- sites.df %>%
mutate( region = sub("_$","",gsub('[[:digit:]"]+', '', cat_ID_con)),
size_diff = case_when(area_diff >0~'Larger', area_diff <0~'Smaller',
TRUE~'No Change')) %>%
subset(select = site_keep_cols )
dput(colnames(cov.df.raw))## c("cat_ID_con", "cat_ID_txt", "cat_ID", "region", "cat_slope_COUNT",
## "cat_slope_AREA", "cat_slope_MIN", "cat_slope_MAX", "cat_slope_RANGE",
## "cat_slope_MEAN", "cat_slope_STD", "cat_slope_SUM", "cat_slope_MEDIAN",
## "cat_slope_PCT90", "cat_elev_COUNT", "cat_elev_AREA", "cat_elev_MIN",
## "cat_elev_MAX", "cat_elev_RANGE", "cat_elev_MEAN", "cat_elev_STD",
## "cat_elev_SUM", "cat_elev_VARIETY", "cat_elev_MAJORITY", "cat_elev_MINORITY",
## "cat_elev_MEDIAN", "cat_elev_PCT90", "wtd_elev_COUNT", "wtd_elev_AREA",
## "wtd_elev_MIN", "wtd_elev_MAX", "wtd_elev_RANGE", "wtd_elev_MEAN",
## "wtd_elev_STD", "wtd_elev_SUM", "wtd_elev_VARIETY", "wtd_elev_MAJORITY",
## "wtd_elev_MINORITY", "wtd_elev_MEDIAN", "wtd_elev_PCT90", "wtd_slope_COUNT",
## "wtd_slope_AREA", "wtd_slope_MIN", "wtd_slope_MAX", "wtd_slope_RANGE",
## "wtd_slope_MEAN", "wtd_slope_STD", "wtd_slope_SUM", "wtd_slope_MEDIAN",
## "wtd_slope_PCT90", "non_north_area", "north_area", "wtd_north_per",
## "non_wetland_area", "wetland_area", "wtd_wet_per", "wtd_lake_area_sqm",
## "wtd_lake_per", "wtd_glacier_area_sqm", "wtd_glacier_per")wtd_keep_cols <- c("cat_ID_con","cat_ID","region", "cat_slope_MIN", "cat_slope_MAX",
"cat_slope_MEAN", "cat_slope_STD", "cat_slope_SUM", "cat_elev_MIN",
"cat_elev_MAX", "cat_elev_MEAN", "cat_elev_STD", "wtd_elev_MIN",
"wtd_elev_MAX", "wtd_elev_MEAN", "wtd_elev_STD", "wtd_slope_MIN",
"wtd_slope_MAX", "wtd_slope_MEAN", "wtd_slope_STD", "wtd_north_per",
"wtd_wet_per","wtd_lake_per","wtd_glacier_per","wtd_area_sqKM")
cov.df <- cov.df.raw %>%
mutate(wtd_area_sqKM = wtd_elev_AREA *1e-6) %>%
subset(select = wtd_keep_cols )
# Convert na to 0
cov.df[is.na(cov.df)] <- 0Site Accumulation Area vs Wtd Area
Compare watershed area (area_km2) as calcuated from shape area in GIS to area calculated from the max flow accumulation () for a given site. Flow accumulation was extracted using zonal statistics as table on a 30 meter site buffer (clipped to catchment) on the appropriate flow accumulation grid, TauDEM derived or NHDPlus depending on region being analyzed.
Definitely some outliers that need to be Identified and examined in GIS
#Function to plot data
ggplotRegression <- function (fit) {
require(ggplot2)
ggplot(fit$model, aes_string(x = names(fit$model)[2], y = names(fit$model)[1])) +
geom_point() +
stat_smooth(method = "lm", col = "red") +
labs(title = paste("Adj R2 = ",signif(summary(fit)$adj.r.squared, 5),
"Intercept =",signif(fit$coef[[1]],5 ),
" Slope =",signif(fit$coef[[2]], 5),
" P =",signif(summary(fit)$coef[2,4], 5)))
}
site_flow_regr <-ggplotRegression(lm(Area_km2 ~site_acc_sqKm, data = sites.df))
site_flow_regr## `geom_smooth()` using formula 'y ~ x'
areafig <- plot_ly(
sites.df, y = ~Area_km2 ,x =~site_acc_sqKm,
#Hover Text
text = ~paste("SiteID:", SiteID,'<br>AreaDiff:', area_diff),
color = ~region, size = ~area_diff, symbol = ~size_diff
)
areafig## No trace type specified:
## Based on info supplied, a 'scatter' trace seems appropriate.
## Read more about this trace type -> https://plotly.com/r/reference/#scatter## No scatter mode specifed:
## Setting the mode to markers
## Read more about this attribute -> https://plotly.com/r/reference/#scatter-mode## Warning: `line.width` does not currently support multiple values.
## Warning: `line.width` does not currently support multiple values.
## Warning: `line.width` does not currently support multiple values.
## Warning: `line.width` does not currently support multiple values.
## Warning: `line.width` does not currently support multiple values.
## Warning: `line.width` does not currently support multiple values.DataExplorer Report for dataset
#use DataExplorer package to examine data
create_report(cov.df, output_file = "AKSSF_Covariate_DataExplore.html")##
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## "C:/Program Files/RStudio/bin/pandoc/pandoc" +RTS -K512m -RTS "C:/Users/dwmerrigan/Documents/GitHub/AKSSF/data_preparation/sensitivity_drivers/report.knit.md" --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output pandoc6e64acc178.html --lua-filter "C:\Users\dwmerrigan\Documents\R\win-library\4.1\rmarkdown\rmarkdown\lua\pagebreak.lua" --lua-filter "C:\Users\dwmerrigan\Documents\R\win-library\4.1\rmarkdown\rmarkdown\lua\latex-div.lua" --self-contained --variable bs3=TRUE --standalone --section-divs --table-of-contents --toc-depth 6 --template "C:\Users\dwmerrigan\Documents\R\win-library\4.1\rmarkdown\rmd\h\default.html" --no-highlight --variable highlightjs=1 --variable theme=yeti --include-in-header "C:\Users\DWMERR~1\AppData\Local\Temp\Rtmp0YJ7sW\rmarkdown-str6e641efe4e93.html" --mathjax --variable "mathjax-url:https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"create_report(sites.df, output_format = html_document(),output_file = "AKSSF_Sites_DataExplore.html")##
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## "C:/Program Files/RStudio/bin/pandoc/pandoc" +RTS -K512m -RTS "C:/Users/dwmerrigan/Documents/GitHub/AKSSF/data_preparation/sensitivity_drivers/report.knit.md" --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output pandoc6e64635923f5.html --lua-filter "C:\Users\dwmerrigan\Documents\R\win-library\4.1\rmarkdown\rmarkdown\lua\pagebreak.lua" --lua-filter "C:\Users\dwmerrigan\Documents\R\win-library\4.1\rmarkdown\rmarkdown\lua\latex-div.lua" --self-contained --variable bs3=TRUE --standalone --section-divs --template "C:\Users\dwmerrigan\Documents\R\win-library\4.1\rmarkdown\rmd\h\default.html" --no-highlight --variable highlightjs=1 --variable theme=bootstrap --include-in-header "C:\Users\DWMERR~1\AppData\Local\Temp\Rtmp0YJ7sW\rmarkdown-str6e641bd76423.html" --mathjax --variable "mathjax-url:https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"#use DataExplorer package to examine data
# Could also loop through regions and spit out regional reports
regions <- c("Kodiak", "Copper_River", "Prince_William_Sound", "Cook_Inlet", "Bristol_Bay")
for (r in regions){
print(r)
df <- cov.df %>%
filter(region == r)
name <- paste(r,'_DataExplore.html', sep = "", collapse = "")
create_report(df, output_format = html_document(),output_file = name)
print (name)
}## [1] "Kodiak"
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## [1] "Copper_River"
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## "C:/Program Files/RStudio/bin/pandoc/pandoc" +RTS -K512m -RTS "C:/Users/dwmerrigan/Documents/GitHub/AKSSF/data_preparation/sensitivity_drivers/report.knit.md" --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output pandoc6e6475a73fcc.html --lua-filter "C:\Users\dwmerrigan\Documents\R\win-library\4.1\rmarkdown\rmarkdown\lua\pagebreak.lua" --lua-filter "C:\Users\dwmerrigan\Documents\R\win-library\4.1\rmarkdown\rmarkdown\lua\latex-div.lua" --self-contained --variable bs3=TRUE --standalone --section-divs --template "C:\Users\dwmerrigan\Documents\R\win-library\4.1\rmarkdown\rmd\h\default.html" --no-highlight --variable highlightjs=1 --variable theme=bootstrap --include-in-header "C:\Users\DWMERR~1\AppData\Local\Temp\Rtmp0YJ7sW\rmarkdown-str6e6468b83d1.html" --mathjax --variable "mathjax-url:https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"
## [1] "Copper_River_DataExplore.html"
## [1] "Prince_William_Sound"
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## "C:/Program Files/RStudio/bin/pandoc/pandoc" +RTS -K512m -RTS "C:/Users/dwmerrigan/Documents/GitHub/AKSSF/data_preparation/sensitivity_drivers/report.knit.md" --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output pandoc6e64359f197e.html --lua-filter "C:\Users\dwmerrigan\Documents\R\win-library\4.1\rmarkdown\rmarkdown\lua\pagebreak.lua" --lua-filter "C:\Users\dwmerrigan\Documents\R\win-library\4.1\rmarkdown\rmarkdown\lua\latex-div.lua" --self-contained --variable bs3=TRUE --standalone --section-divs --template "C:\Users\dwmerrigan\Documents\R\win-library\4.1\rmarkdown\rmd\h\default.html" --no-highlight --variable highlightjs=1 --variable theme=bootstrap --include-in-header "C:\Users\DWMERR~1\AppData\Local\Temp\Rtmp0YJ7sW\rmarkdown-str6e64727f1767.html" --mathjax --variable "mathjax-url:https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"
## [1] "Prince_William_Sound_DataExplore.html"
## [1] "Cook_Inlet"
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## "C:/Program Files/RStudio/bin/pandoc/pandoc" +RTS -K512m -RTS "C:/Users/dwmerrigan/Documents/GitHub/AKSSF/data_preparation/sensitivity_drivers/report.knit.md" --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output pandoc6e64e464632.html --lua-filter "C:\Users\dwmerrigan\Documents\R\win-library\4.1\rmarkdown\rmarkdown\lua\pagebreak.lua" --lua-filter "C:\Users\dwmerrigan\Documents\R\win-library\4.1\rmarkdown\rmarkdown\lua\latex-div.lua" --self-contained --variable bs3=TRUE --standalone --section-divs --template "C:\Users\dwmerrigan\Documents\R\win-library\4.1\rmarkdown\rmd\h\default.html" --no-highlight --variable highlightjs=1 --variable theme=bootstrap --include-in-header "C:\Users\DWMERR~1\AppData\Local\Temp\Rtmp0YJ7sW\rmarkdown-str6e6461db7e27.html" --mathjax --variable "mathjax-url:https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"
## [1] "Cook_Inlet_DataExplore.html"
## [1] "Bristol_Bay"
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## "C:/Program Files/RStudio/bin/pandoc/pandoc" +RTS -K512m -RTS "C:/Users/dwmerrigan/Documents/GitHub/AKSSF/data_preparation/sensitivity_drivers/report.knit.md" --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output pandoc6e6470841dca.html --lua-filter "C:\Users\dwmerrigan\Documents\R\win-library\4.1\rmarkdown\rmarkdown\lua\pagebreak.lua" --lua-filter "C:\Users\dwmerrigan\Documents\R\win-library\4.1\rmarkdown\rmarkdown\lua\latex-div.lua" --self-contained --variable bs3=TRUE --standalone --section-divs --template "C:\Users\dwmerrigan\Documents\R\win-library\4.1\rmarkdown\rmd\h\default.html" --no-highlight --variable highlightjs=1 --variable theme=bootstrap --include-in-header "C:\Users\DWMERR~1\AppData\Local\Temp\Rtmp0YJ7sW\rmarkdown-str6e64697342c3.html" --mathjax --variable "mathjax-url:https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"
## [1] "Bristol_Bay_DataExplore.html"Data by region
dat_cols <- c("region", "cat_slope_MIN", "cat_slope_MAX",
"cat_slope_MEAN", "cat_slope_STD", "cat_slope_SUM", "cat_elev_MIN",
"cat_elev_MAX", "cat_elev_MEAN", "cat_elev_STD", "wtd_elev_MIN",
"wtd_elev_MAX", "wtd_elev_MEAN", "wtd_elev_STD", "wtd_slope_MIN",
"wtd_slope_MAX", "wtd_slope_MEAN", "wtd_slope_STD", "wtd_north_per",
"wtd_wet_per","wtd_lake_per","wtd_glacier_per","wtd_area_sqKM")
cov.df.explore <- cov.df[,dat_cols]
plot_qq(cov.df.explore, by = "region")
### Bristol Bay
reg = 'Bristol_Bay'
bb_miss <- cov.df.explore %>%
filter(region == reg) %>%
plot_missing()
bb_histo <- cov.df.explore %>%
filter(region == reg) %>%
plot_histogram()
bb_qq <- cov.df.explore %>%
filter(region == reg) %>%
plot_qq()
bb_co <- cov.df.explore %>%
filter(region == reg)
plot_correlation(na.omit(bb_co))## Warning in cor(x = structure(list(cat_slope_MIN = c(7.77573134769227e-08, : the
## standard deviation is zero
### Kodiak
reg1 = 'Kodiak'
ko_miss <- cov.df.explore %>%
filter(region == reg1) %>%
plot_missing()
ko_histo <- cov.df.explore %>%
filter(region == reg1) %>%
plot_histogram()
ko_qq <- cov.df.explore %>%
filter(region == reg1) %>%
plot_qq()
ko_co <- cov.df.explore %>%
filter(region == reg1)
plot_correlation(na.omit(ko_co))## Warning in cor(x = structure(list(cat_slope_MIN = c(2.20679410745106e-07, : the
## standard deviation is zero
PWS
reg2 = 'Prince_William_Sound'
pws_miss <- cov.df.explore %>%
filter(region == reg2) %>%
plot_missing()
pws_histo <- cov.df.explore %>%
filter(region == reg2) %>%
plot_histogram()
pws_qq <- cov.df.explore %>%
filter(region == reg2) %>%
plot_qq()
pws_co <- cov.df.explore %>%
filter(region == reg2)
plot_correlation(na.omit(pws_co))## Warning in cor(x = structure(list(cat_slope_MIN = c(1.03179516486307e-07, : the
## standard deviation is zero
### Cook Inlet
reg3 = 'Cook_Inlet'
ci_miss <- cov.df.explore %>%
filter(region == reg3) %>%
plot_missing()
ci_histo <- cov.df.explore %>%
filter(region == reg3) %>%
plot_histogram()
ci_qq <- cov.df.explore %>%
filter(region == reg3) %>%
plot_qq()
ci_co <- cov.df.explore %>%
filter(region == reg3)
plot_correlation(na.omit(ci_co))## Warning in cor(x = structure(list(cat_slope_MIN = c(1.54075132741127e-06, : the
## standard deviation is zero
Copper River
reg4 = 'Copper_River'
co_miss <- cov.df.explore %>%
filter(region == reg4) %>%
plot_missing()
co_histo <- cov.df.explore %>%
filter(region == reg4) %>%
plot_histogram()
co_qq <- cov.df.explore %>%
filter(region == reg4) %>%
plot_qq()
co_co <- cov.df.explore %>%
filter(region == reg4)
plot_correlation(na.omit(co_co))## Warning in cor(x = structure(list(cat_slope_MIN = c(1.62371288752183e-05, : the
## standard deviation is zero